One of the most important problem of misspecification in the probit model is the correlation between regressors and error term. To deal with this problem, some commercial software gives a solution such as Stata. For the famous R language the ivprobit gives the users the way to estimate the instrumental probit model.
{"title":"ivporbit: An R Package to Estimate the Probit Model with Continuous Endogenous Regressors","authors":"Taha Zaghdoudi","doi":"10.2139/ssrn.2811749","DOIUrl":"https://doi.org/10.2139/ssrn.2811749","url":null,"abstract":"One of the most important problem of misspecification in the probit model is the correlation between regressors and error term. To deal with this problem, some commercial software gives a solution such as Stata. For the famous R language the ivprobit gives the users the way to estimate the instrumental probit model.","PeriodicalId":438593,"journal":{"name":"ERN: Econometric Software (Topic)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114611151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-11-24DOI: 10.1108/S0731-9053(2012)0000030008
J. Lye, J. Hirschberg
In this chapter we demonstrate the construction of inverse test confidence intervals for the turning-points in estimated nonlinear relationships by the use of the marginal or first derivative function. First, we outline the inverse test confidence interval approach. Then we examine the relationship between the traditional confidence intervals based on the Wald test for the turning-points for a cubic, a quartic, and fractional polynomials estimated via regression analysis and the inverse test intervals. We show that the confidence interval plots of the marginal function can be used to estimate confidence intervals for the turning-points that are equivalent to the inverse test. We also provide a method for the interpretation of the confidence intervals for the second derivative function to draw inferences for the characteristics of the turning-point. This method is applied to the examination of the turning-points found when estimating a quartic and a fractional polynomial from data used for the estimation of an Environmental Kuznets Curve. The Stata do files used to generate these examples are listed in Appendix A along with the data.
{"title":"Inverse Test Confidence Intervals for Turning-Points: A Demonstration with Higher Order Polynomials","authors":"J. Lye, J. Hirschberg","doi":"10.1108/S0731-9053(2012)0000030008","DOIUrl":"https://doi.org/10.1108/S0731-9053(2012)0000030008","url":null,"abstract":"In this chapter we demonstrate the construction of inverse test confidence intervals for the turning-points in estimated nonlinear relationships by the use of the marginal or first derivative function. First, we outline the inverse test confidence interval approach. Then we examine the relationship between the traditional confidence intervals based on the Wald test for the turning-points for a cubic, a quartic, and fractional polynomials estimated via regression analysis and the inverse test intervals. We show that the confidence interval plots of the marginal function can be used to estimate confidence intervals for the turning-points that are equivalent to the inverse test. We also provide a method for the interpretation of the confidence intervals for the second derivative function to draw inferences for the characteristics of the turning-point. \u0000 \u0000This method is applied to the examination of the turning-points found when estimating a quartic and a fractional polynomial from data used for the estimation of an Environmental Kuznets Curve. The Stata do files used to generate these examples are listed in Appendix A along with the data.","PeriodicalId":438593,"journal":{"name":"ERN: Econometric Software (Topic)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127723877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An Eviews program is provided that computes the BLUS residuals. A dataset is provided to confirm the output, which is compared to the output of the BLUS function in SAS.
{"title":"BLUS Residuals for Eviews","authors":"George S. Ford","doi":"10.2139/ssrn.1293947","DOIUrl":"https://doi.org/10.2139/ssrn.1293947","url":null,"abstract":"An Eviews program is provided that computes the BLUS residuals. A dataset is provided to confirm the output, which is compared to the output of the BLUS function in SAS.","PeriodicalId":438593,"journal":{"name":"ERN: Econometric Software (Topic)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123898109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2005-05-01DOI: 10.1504/IJPQM.2006.008480
Avi Giloni, S. Seshadri, J. Simonoff
We discuss the use of robust Analysis Of Variance (ANOVA) techniques as applied to quality engineering. ANOVA is the cornerstone for uncovering the effects of design factors on performance. Our goal is to utilise methodologies that yield similar results to standard methods when the underlying assumptions are satisfied, but are also relatively unaffected by outliers (observations that are inconsistent with the general pattern in the data). We do this by utilising statistical software to implement robust ANOVA methods, which are no more difficult to perform than ordinary ANOVA. We study several examples to illustrate how using standard techniques can lead to misleading inferences about the process being examined, which are avoided when using a robust analysis. We further demonstrate that assessments of the importance of factors for quality design can be seriously compromised when utilising standard methods as opposed to robust methods.
{"title":"Robust Analysis of Variance: Process Design and Quality Improvement","authors":"Avi Giloni, S. Seshadri, J. Simonoff","doi":"10.1504/IJPQM.2006.008480","DOIUrl":"https://doi.org/10.1504/IJPQM.2006.008480","url":null,"abstract":"We discuss the use of robust Analysis Of Variance (ANOVA) techniques as applied to quality engineering. ANOVA is the cornerstone for uncovering the effects of design factors on performance. Our goal is to utilise methodologies that yield similar results to standard methods when the underlying assumptions are satisfied, but are also relatively unaffected by outliers (observations that are inconsistent with the general pattern in the data). We do this by utilising statistical software to implement robust ANOVA methods, which are no more difficult to perform than ordinary ANOVA. We study several examples to illustrate how using standard techniques can lead to misleading inferences about the process being examined, which are avoided when using a robust analysis. We further demonstrate that assessments of the importance of factors for quality design can be seriously compromised when utilising standard methods as opposed to robust methods.","PeriodicalId":438593,"journal":{"name":"ERN: Econometric Software (Topic)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132358135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Towards an Emergence-Driven Software Process for Agent-Based Simulation","authors":"N. David, Jaime Simão Sichman, H. Coelho","doi":"10.1007/3-540-36483-8_7","DOIUrl":"https://doi.org/10.1007/3-540-36483-8_7","url":null,"abstract":"","PeriodicalId":438593,"journal":{"name":"ERN: Econometric Software (Topic)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117163305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：